Role of arthroscopy for the diagnosis and management of post-traumatic hip pain: a prospective study
Bibliographic record
Abstract
Abstract The current published literature regarding the role of hip arthroscopy in the diagnosis and management of post-traumatic hip pain is still limited. Therefore, we conducted the present prospective study to determine the value of hip arthroscopy in the diagnosis and management of various causes of hip pain after traumatic conditions. The present study included a prospective cohort of 17 patients with symptomatic post-traumatic hip pain. It was conducted between July 2013 and May 2018. The mean age was 22 (19–29) years and the mean follow-up was 24 (r: 7–36) months. Prior to surgery, every eligible patient underwent assessment of functional status using the Modified Harris Hip Score, Oxford hip score (OHS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) score. All patients underwent arthroscopic management for their diagnosed pathologies. The most commonly encountered diagnosis was labral tear (58.8%), followed by ligamentum teres tear (35.3%) and loose intra-articular fragments (29.4%). In addition, 52.9% of the patients had associated CAM lesion and 11.8% had associated Pincer lesion. The mHSS, OHS and WOMAC score showed significant improvement in the post-operative period (P < 0.001), all the 17 patients had 100% Patient Acceptable Symptomatic State; only one patient did not achieve minimal clinical importance difference. One case underwent labral debridement for failed labral repair (5.8%), another patient developed maralgia paraesthetica (5.8%). In conclusion, hip arthroscopy is a useful and effective minimally invasive procedure for the diagnosis and management of selected patients with post-traumatic hip pain. Moreover, hip arthroscopy was safe technique with no reported serious adverse events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".